研究目的
To present photovoltaic (PV) string failure analysis and health monitoring of PV modules based on a low-cost self-powered wireless sensor network (WSN), proposing a simple and effective fault detection and diagnosis method based on the real-time operating voltage of PV modules.
研究成果
The proposed method effectively identifies faulty PV modules based on their low operating voltage compared to normal modules. The developed health monitoring system, including a GUI program, facilitates easy identification of faulty modules. The approach is verified using EL imaging, confirming its effectiveness. The solution is particularly useful for small-to medium-scale PV systems due to its low cost and effectiveness.
研究不足
The study focuses on the identification of faulty modules based on operating voltage differences, which may not detect all types of faults. The system's effectiveness is demonstrated in a specific grid-connected PV system, and its performance in other configurations or environments may vary.
1：Experimental Design and Method Selection:
The study involves the development of a health monitoring system for PV modules using a wireless sensor network (WSN) to monitor the real-time operating voltage of each module. The method is verified by installing the system in a grid-connected PV system.
2：Sample Selection and Data Sources:
Eleven Mitsubishi Electric PV modules (PV-AE125MF5N) are used in a grid-connected PV system. Data is collected on the operating voltage of each module under various conditions.
3：List of Experimental Equipment and Materials:
The WSN consists of sensor nodes, cluster-heads, and a coordinator built using Arduino Nano, LM2596 step-down converter, ACS712 10A current sensor, and NRF24L01 wireless transceiver module.
4：Experimental Procedures and Operational Workflow:
The system monitors the voltage of each PV module via WSN, with data sent to a monitoring station for analysis. A GUI program displays the real-time operating voltage of each module.
5：Data Analysis Methods:
The operating voltage of each module is analyzed to identify faulty modules, with results verified using electroluminescence (EL) imaging.
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